查看更多>>摘要：New research on robotics is the subject of a new report. According to news originating from the Council of Scientific and Industrial Research (CSIR) by NewsRx correspondents, research stated, "Due to the high prevalence and unpredictability of violent protest action in South Africa, a need has arisen for rapidly deployable surveillance." The news journalists obtained a quote from the research from Council of Scientific and Industrial Research (CSIR): "This paper proposes an Automated Robotic Guardian for Urban Surveillance (ARGUS) platform, a wheeled, pole climbing robot as a potential solution. The platform is designed to attach to and traverse up existing cylindrically shaped infrastructure, such as light posts, enabling easy deployment in urban environments. The robot is intended for various surveillance needs, such as public safety at events, and periods of unrest or protest action. Following a detailed concept design stage, simulated results are presented for the proposed robot. This includes comprehensive CAD modelling, static force and torque calculations of the pole climbing robot, and finite element analysis of the component stresses while positioned on the pole."
查看更多>>摘要：Investigators publish new report on robotics. According to news reporting from Lomonosov Moscow State University by NewsRx journalists, research stated, "The world continues to actively develop robots for agriculture." The news correspondents obtained a quote from the research from Lomonosov Moscow State University: "The concept of unmanned technologies is being promoted in the agricultural sector of developed countries. Its high efficiency is expected due to the reduction of labor costs. However, the implementation of this concept in practice faces difficulties. The cost of creating new intellectual property results increases every year. To create a robot and ensure its novelty, inventors are forced to spend more and more money, resort to the services of outside engineers. Plant protection (including potato) remains an acute problem, requiring increased efficiency of treatments."
查看更多>>摘要：Investigators publish new report on Robotics. According to news reporting originating in Edmonton, Canada, by NewsRx journalists, research stated, "In cable-driven parallel robots (CDPRs), the controller should generate positive output forces, as cables only support tensile forces. In fully-constrained CDPRs, positive tension distribution in cables is guaranteed using optimization-based techniques, which have unpredictable worst-case computation time." The news reporters obtained a quote from the research from the University of Alberta, "Furthermore, the optimization-based methods fail to handle situations where the initial pose of the end-effector is beyond the wrench-feasible workspace (WFW). To address the existing problems, we introduce a new representation of the dynamic model of CDPRs by including cable tension positiveness as an inherent part of the dynamic, using an absolute function. This leads to a non-affine dynamic model, which is then converted to the affine form, for which a robust super-twisting sliding mode controller is designed. The stability of the closed-loop system is guaranteed via the Lyapunov direct method, where H1 asymptotic stability is proved with parameters derived by solving a linear matrix inequality. The superiority of the proposed method is validated in both simulation and experiment. The analytical nature of the method also allows dramatic improvement in the computation time of the control compared to the optimization-based methods in the literature."
查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting out of Mumbai, India, by NewsRx editors, research stated, "We investigated the accelerated prediction of the thermal conductivity of materials through end-to-end structure-based approaches employing machine learning methods. Due to the non-availability of high-quality thermal conductivity data, we first performed high-throughput calculations based on first principles and the Boltzmann transport equation for 225 materials, effectively more than doubling the size of the existing dataset." Funders for this research include National Supercomputing Mission, Government of India, Core Research Grant, Science & Engineering Research Board, India, Nano Mission, Government of India.
查看更多>>摘要：Researchers detail new data in Machine Learning. According to news originating from Hangzhou, People's Republic of China, by NewsRx correspondents, research stated, "Probabilistic forecasting provides complete probability information of renewable generation and load, which assists the diverse decision-making tasks in power systems under uncertainties. Conventional machine learning-based probabilistic forecasting methods usually consider the predictive uncertainty following prior distributional assumptions." Our news journalists obtained a quote from the research from Zhejiang University, "This article develops a novel combined bootstrap and cumulant (CBC) method to generate nonparametric predictive distribution using higher order statistics for probabilistic forecasting. The CBC method successfully integrates machine learning with conditional moments and cumulants to describe the overall predictive uncertainty. A bootstrap-based conditional moment estimation method is proposed to quantify both the epistemic and aleatory uncertainties involved in machine learning. Higher order cumulants are utilized for overall uncertainty quantification based on the estimated conditional moments with its unique additivity. Three types of series expansions including Gram-Charlier, Edgeworth, and Cornish-Fisher expansions are adopted to improve the overall performance and the generalization ability."
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news originating from Havana, Cuba, by NewsRx correspondents, research stated, "Falls have a global impact, affecting people worldwide, with a notably high occurrence among the elderly. This study employs machine learning techniques to analyze falls and simulate Activities of Daily Living (ADL)." Financial support for this research came from Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ). Our news journalists obtained a quote from the research from the Institute of Science & Technology, "The objective is to predict human falls by leveraging signals from accelerometers and gyroscopes as wearable sensors. By deriving statistical features such as mean, standard deviation, and range the authors successfully trained and assessed six machine learning models allowing them to compare solutions based on both wrist and waist data. The combination of these characteristics and sensors resulted in the Random Forest waist model achieving the most favorable metrics, with an accuracy rate of 97.22% in a 5-s window. Falls are a widespread issue affecting people worldwide, regardless of their social status. This study presents various ML models, which can predict human falls using signals of a wearable sensor located on the wrist or the waist."
查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting from Yerevan, Armenia, by NewsRx journalists, research stated, "In this research, we employed a deep reinforcement learning (RL)-based molecule design platform to generate a diverse set of compounds targeting the neuraminidase (NA) of influenza A and B viruses. A total of 60,291 compounds were generated, of which 86.5 % displayed superior physicochemical properties compared to oseltamivir." The news correspondents obtained a quote from the research, "After narrowing down the selection through computational filters, nine compounds with non-sialic acid-like structures were selected for in vitro experiments. We identified two compounds, DS-22-inf-009 and DS-22-inf-021 that effectively inhibited the NAs of both influenza A and B viruses (IAV and IBV), including H275Y mutant strains at low micromolar concentrations. Molecular dynamics simulations revealed a similar pattern of interaction with amino acid residues as oseltamivir. In cell-based assays, DS-22-inf-009 and DS-22-inf-021 inhibited IAV and IBV in a dose-dependent manner with EC values ranging from 0.29 mM to 2.31 mM. Furthermore, animal experiments showed that both DS-22-inf-009 and DS-22-inf-021 exerted antiviral activity in mice, conferring 65 % and 85 % protection from IAV (H1N1 pdm09), and 65 % and 100 % protection from IBV (Yamagata lineage), respectively."
查看更多>>摘要：Research findings on Robotics are discussed in a new report. According to news reporting out of Beijing, People's Republic of China, by NewsRx editors, research stated, "The important indicators to measure the goodness of rigid fruit and vegetable picking robot have two aspects, the first is the reasonable structural design of the end-effector, and the second is having a high precision positioning recognition method." Funders for this research include National Natural Science Foundation of China (NSFC), The 14th Five-Year Plan of Beijing Education Science. Our news journalists obtained a quote from the research from the North China University of Technology, "Many researchers have done a lot of work in these two aspects, and a variety of end-effector structures and advanced position recognition methods are constantly appearing in people's view. The working principle, structural characteristics, advantages and disadvantages of each end-effector are summarized to help us design better fruit and vegetable picking robot."
查看更多>>摘要：Investigators discuss new findings in . According to news reporting out of Orem, Utah, by NewsRx editors, research stated, "Traditionally, condition monitoring of wind turbines has been performed manually by certified rope teams. This method of inspection can be dangerous for the personnel involved, and the resulting downtime can be expensive." Financial supporters for this research include Office of The Commissioner of Utah System of Higher Education (Ushe)-deep Technology Initiative. The news reporters obtained a quote from the research from Utah Valley University: "Wind turbine inspection can be performed using autonomous drones to achieve lower downtime, cost, and health risks. To enable autonomy, the field of drone path planning can be assisted by this research, namely machine learning that detects wind turbines present in aerial RGB images taken by the drone before performing the maneuvering for turbine inspection. For this task, the effectiveness of two deep learning architectures is evaluated in this paper both without and with a proposed fuzzy contrast enhancement (FCE) image preprocessing algorithm. Efforts are focused on two convolutional neural network (CNN) variants: VGG19 and Xception. A more traditional approach involving support vector machines (SVM) is also included to contrast a machine learning approach with our deep learning networks. The authors created a novel dataset of 4500 RGB images of size 210 x 210 to train and evaluate the performance of these networks on wind turbine detection. The dataset is captured in an environment mimicking that of a wind turbine farm, and consists of two classes of images: with and without a small-scale wind turbine (12V Primus Air Max) assembled at Utah Valley University."
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news originating from Bloomington, Indiana, by NewsRx correspondents, research stated, "We present a quantum mechanical/machine learning (ML) framework based on random forest to accurately predict the ps of complex organic molecules using inexpensive density functional theory (DFT) calculations. By including physics-based features from low-level DFT calculations and structural features from our connectivity-based hierarchy (CBH) fragmentation protocol, we can correct the systematic error associated with DFT." Our news journalists obtained a quote from the research from the Department of Chemistry, "The generalizability and performance of our model are evaluated on two benchmark sets (SAMPL6 and Novartis). We believe the carefully curated input of physics-based features lessens the model's data dependence and need for complex deep learning architectures, without compromising the accuracy of the test sets."
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